{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Action1_LSTM_(30,30)_meijimu_stock\n",
    "\n",
    "美吉姆股票价格预测"
   ]
  },
  {
   "attachments": {
    "607324ae-42c1-416a-8b9e-ee12dad22605.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本题采用LSTM预测30天的股票价格，时间步入出为(30-->30),多对多\n",
    "\n",
    "多对多例图(3-->2)\n",
    "\n",
    "![image.png](attachment:607324ae-42c1-416a-8b9e-ee12dad22605.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用LSTM预测沪市指数\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import LSTM\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from pandas import DataFrame\n",
    "from pandas import concat\n",
    "from itertools import chain\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 转化为可以用于监督学习的数据\n",
    "def get_train_set(data_set, timesteps_in, timesteps_out=1):\n",
    "    train_data_set = np.array(data_set)\n",
    "    reframed_train_data_set = np.array(series_to_supervised(train_data_set, timesteps_in, timesteps_out).values)\n",
    "    print(reframed_train_data_set)\n",
    "    print(reframed_train_data_set.shape)\n",
    "    train_x, train_y = reframed_train_data_set[:, :-timesteps_out], reframed_train_data_set[:, -timesteps_out:]\n",
    "    # 将数据集重构为符合LSTM要求的数据格式,即 [样本数，时间步，特征]\n",
    "    train_x = train_x.reshape((train_x.shape[0], timesteps_in, 1))\n",
    "    return train_x, train_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[23.98, 23.1 , 22.66, 24.25, 23.6 , 24.48],\n",
       "       [23.1 , 22.66, 24.25, 23.6 , 24.48, 25.47],\n",
       "       [22.66, 24.25, 23.6 , 24.48, 25.47, 25.3 ],\n",
       "       ...,\n",
       "       [ 6.5 ,  6.53,  6.73,  6.99,  6.95,  6.93],\n",
       "       [ 6.53,  6.73,  6.99,  6.95,  6.93,  7.02],\n",
       "       [ 6.73,  6.99,  6.95,  6.93,  7.02,  7.  ]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg.values"
   ]
  },
  {
   "attachments": {
    "d3e0b854-2170-4b44-96b0-3b61ad7f9725.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:d3e0b854-2170-4b44-96b0-3b61ad7f9725.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[23.98, 23.1 , 22.66],\n",
       "       [23.1 , 22.66, 24.25],\n",
       "       [22.66, 24.25, 23.6 ],\n",
       "       ...,\n",
       "       [ 6.5 ,  6.53,  6.73],\n",
       "       [ 6.53,  6.73,  6.99],\n",
       "       [ 6.73,  6.99,  6.95]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train_x\n",
    "agg.values[:,:-3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1963,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(data_set).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 通过入步和出步，主要利用pd.shift()进行错步填充，以此来分x_train,y_label\n",
    "\"\"\"\n",
    "将时间序列数据转换为适用于监督学习的数据\n",
    "给定输入、输出序列的长度\n",
    "data: 观察序列\n",
    "n_in: 观测数据input(X)的步长，范围[1, len(data)], 默认为1\n",
    "n_out: 观测数据output(y)的步长， 范围为[0, len(data)-1], 默认为1\n",
    "dropnan: 是否删除NaN行\n",
    "返回值：适用于监督学习的 DataFrame\n",
    "\"\"\"\n",
    "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
    "    n_vars = 1 if type(data) is list else data.shape[1]\n",
    "    df = DataFrame(data)\n",
    "    cols, names = list(), list()\n",
    "    # input sequence (t-n, ... t-1)\n",
    "    for i in range(n_in, 0, -1):\n",
    "        cols.append(df.shift(i)) # DataFrame列往下错位一格，并用Nan填充第i行，丢失掉之前的最后i行\n",
    "        names += [('var%d(t-%d)' % (j + 1, i)) for j in range(n_vars)]\n",
    "    # 预测序列 (t, t+1, ... t+n)\n",
    "    for i in range(0, n_out):\n",
    "        cols.append(df.shift(-i))\n",
    "        if i == 0:\n",
    "            names += [('var%d(t)' % (j + 1)) for j in range(n_vars)]\n",
    "        else:\n",
    "            names += [('var%d(t+%d)' % (j + 1, i)) for j in range(n_vars)]\n",
    "    # 拼接到一起\n",
    "    global agg\n",
    "    agg = concat(cols, axis=1)\n",
    "    agg.columns = names\n",
    "    # 去掉NaN行\n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var1(t-3)</th>\n",
       "      <th>var1(t-2)</th>\n",
       "      <th>var1(t-1)</th>\n",
       "      <th>var1(t)</th>\n",
       "      <th>var1(t+1)</th>\n",
       "      <th>var1(t+2)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.98</td>\n",
       "      <td>23.10</td>\n",
       "      <td>22.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.98</td>\n",
       "      <td>23.10</td>\n",
       "      <td>22.66</td>\n",
       "      <td>24.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>23.98</td>\n",
       "      <td>23.10</td>\n",
       "      <td>22.66</td>\n",
       "      <td>24.25</td>\n",
       "      <td>23.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.98</td>\n",
       "      <td>23.10</td>\n",
       "      <td>22.66</td>\n",
       "      <td>24.25</td>\n",
       "      <td>23.60</td>\n",
       "      <td>24.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.10</td>\n",
       "      <td>22.66</td>\n",
       "      <td>24.25</td>\n",
       "      <td>23.60</td>\n",
       "      <td>24.48</td>\n",
       "      <td>25.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>6.50</td>\n",
       "      <td>6.53</td>\n",
       "      <td>6.73</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.95</td>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2192</th>\n",
       "      <td>6.53</td>\n",
       "      <td>6.73</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.95</td>\n",
       "      <td>6.93</td>\n",
       "      <td>7.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2193</th>\n",
       "      <td>6.73</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.95</td>\n",
       "      <td>6.93</td>\n",
       "      <td>7.02</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2194</th>\n",
       "      <td>6.99</td>\n",
       "      <td>6.95</td>\n",
       "      <td>6.93</td>\n",
       "      <td>7.02</td>\n",
       "      <td>7.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2195</th>\n",
       "      <td>6.95</td>\n",
       "      <td>6.93</td>\n",
       "      <td>7.02</td>\n",
       "      <td>7.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2196 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      var1(t-3)  var1(t-2)  var1(t-1)  var1(t)  var1(t+1)  var1(t+2)\n",
       "0           NaN        NaN        NaN    23.98      23.10      22.66\n",
       "1           NaN        NaN      23.98    23.10      22.66      24.25\n",
       "2           NaN      23.98      23.10    22.66      24.25      23.60\n",
       "3         23.98      23.10      22.66    24.25      23.60      24.48\n",
       "4         23.10      22.66      24.25    23.60      24.48      25.47\n",
       "...         ...        ...        ...      ...        ...        ...\n",
       "2191       6.50       6.53       6.73     6.99       6.95       6.93\n",
       "2192       6.53       6.73       6.99     6.95       6.93       7.02\n",
       "2193       6.73       6.99       6.95     6.93       7.02       7.00\n",
       "2194       6.99       6.95       6.93     7.02       7.00        NaN\n",
       "2195       6.95       6.93       7.02     7.00        NaN        NaN\n",
       "\n",
       "[2196 rows x 6 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 去掉nan之前\n",
    "agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var1(t-3)</th>\n",
       "      <th>var1(t-2)</th>\n",
       "      <th>var1(t-1)</th>\n",
       "      <th>var1(t)</th>\n",
       "      <th>var1(t+1)</th>\n",
       "      <th>var1(t+2)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.98</td>\n",
       "      <td>23.10</td>\n",
       "      <td>22.66</td>\n",
       "      <td>24.25</td>\n",
       "      <td>23.60</td>\n",
       "      <td>24.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.10</td>\n",
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       "      <td>23.60</td>\n",
       "      <td>24.48</td>\n",
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       "      <th>5</th>\n",
       "      <td>22.66</td>\n",
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       "      <td>25.47</td>\n",
       "      <td>25.30</td>\n",
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       "      <th>6</th>\n",
       "      <td>24.25</td>\n",
       "      <td>23.60</td>\n",
       "      <td>24.48</td>\n",
       "      <td>25.47</td>\n",
       "      <td>25.30</td>\n",
       "      <td>26.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23.60</td>\n",
       "      <td>24.48</td>\n",
       "      <td>25.47</td>\n",
       "      <td>25.30</td>\n",
       "      <td>26.11</td>\n",
       "      <td>26.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189</th>\n",
       "      <td>6.64</td>\n",
       "      <td>6.66</td>\n",
       "      <td>6.50</td>\n",
       "      <td>6.53</td>\n",
       "      <td>6.73</td>\n",
       "      <td>6.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2190</th>\n",
       "      <td>6.66</td>\n",
       "      <td>6.50</td>\n",
       "      <td>6.53</td>\n",
       "      <td>6.73</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>6.50</td>\n",
       "      <td>6.53</td>\n",
       "      <td>6.73</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.95</td>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2192</th>\n",
       "      <td>6.53</td>\n",
       "      <td>6.73</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.95</td>\n",
       "      <td>6.93</td>\n",
       "      <td>7.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2193</th>\n",
       "      <td>6.73</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.95</td>\n",
       "      <td>6.93</td>\n",
       "      <td>7.02</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2191 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      var1(t-3)  var1(t-2)  var1(t-1)  var1(t)  var1(t+1)  var1(t+2)\n",
       "3         23.98      23.10      22.66    24.25      23.60      24.48\n",
       "4         23.10      22.66      24.25    23.60      24.48      25.47\n",
       "5         22.66      24.25      23.60    24.48      25.47      25.30\n",
       "6         24.25      23.60      24.48    25.47      25.30      26.11\n",
       "7         23.60      24.48      25.47    25.30      26.11      26.25\n",
       "...         ...        ...        ...      ...        ...        ...\n",
       "2189       6.64       6.66       6.50     6.53       6.73       6.99\n",
       "2190       6.66       6.50       6.53     6.73       6.99       6.95\n",
       "2191       6.50       6.53       6.73     6.99       6.95       6.93\n",
       "2192       6.53       6.73       6.99     6.95       6.93       7.02\n",
       "2193       6.73       6.99       6.95     6.93       7.02       7.00\n",
       "\n",
       "[2191 rows x 6 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 去掉nan后，也就是去掉所有的包含nan的行，这里时间步设置(3,3)，上三列，下两列\n",
    "agg"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "时间入步为3的name取值\n",
    "\n",
    "![image.png](attachment:e9ba36df-9e8b-4893-ad2c-88f105e031ee.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用LSTM进行预测\n",
    "def lstm_model(source_data_set, train_x, label_y, input_epochs, input_batch_size, timesteps_out):\n",
    "    model = Sequential()\n",
    "    \n",
    "    # 第一层, 隐藏层神经元节点个数为128, 返回整个序列\n",
    "    model.add(LSTM(128, return_sequences=True, activation='tanh', input_shape=(train_x.shape[1], train_x.shape[2])))\n",
    "    # 第二层，隐藏层神经元节点个数为128, 只返回序列最后一个输出\n",
    "    model.add(LSTM(128, return_sequences=False))\n",
    "    model.add(Dropout(0.5))\n",
    "    # 第三层 因为是回归问题所以使用linear\n",
    "    model.add(Dense(timesteps_out, activation='linear'))\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "\n",
    "    # LSTM训练 input_epochs次数, verbose = 2 为每个epoch输出一行记录, =1为输出进度条记录, =0 不在标准输出流输出日志信息\n",
    "    res = model.fit(train_x, label_y, epochs=input_epochs, batch_size=input_batch_size, verbose=2, shuffle=False)\n",
    "\n",
    "    # 模型预测\n",
    "    train_predict = model.predict(train_x)\n",
    "    #test_data_list = list(chain(*test_data))\n",
    "    train_predict_list = list(chain(*train_predict))\n",
    "\n",
    "    plt.plot(res.history['loss'], label='train')\n",
    "    plt.show()\n",
    "    print(model.summary())\n",
    "    plot_img(source_data_set, train_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 呈现原始数据，训练结果，验证结果，预测结果\n",
    "def plot_img(source_data_set, train_predict):\n",
    "    plt.figure(figsize=(24, 8))\n",
    "#     # 原始数据蓝色\n",
    "#     plt.plot(source_data_set[:, -1], c='b')  # 原始数据有括号，取每一行的括号内数\n",
    "    # 训练集原始数据,shift错位提取数据后、drapna后的数据\n",
    "    plt.plot(agg.iloc[:,0].values[30:],c='g',label='实际数据') # 使用了初始数据dropna后减少59行数据后的\n",
    "    # 预测数据，红色\n",
    "    plt.plot([x for x in train_predict[:,-1]], c='r',label='预测数据')# 这里做了修改，预测结果取预测最后一列\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置观测数据input(X)的步长（时间步），epochs，batch_size\n",
    "timesteps_in = 30\n",
    "timesteps_out = 30\n",
    "epochs = 200\n",
    "batch_size = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./002621.csv')\n",
    "# data_set = data[::-1][['开盘价']].values.astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>股票代码</th>\n",
       "      <th>名称</th>\n",
       "      <th>收盘价</th>\n",
       "      <th>最高价</th>\n",
       "      <th>最低价</th>\n",
       "      <th>开盘价</th>\n",
       "      <th>前收盘</th>\n",
       "      <th>涨跌额</th>\n",
       "      <th>涨跌幅</th>\n",
       "      <th>换手率</th>\n",
       "      <th>成交量</th>\n",
       "      <th>成交金额</th>\n",
       "      <th>总市值</th>\n",
       "      <th>流通市值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2195</th>\n",
       "      <td>2011/9/29</td>\n",
       "      <td>'002621</td>\n",
       "      <td>N三垒</td>\n",
       "      <td>24.08</td>\n",
       "      <td>25.15</td>\n",
       "      <td>22.88</td>\n",
       "      <td>23.98</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>57.5282</td>\n",
       "      <td>11522900</td>\n",
       "      <td>2.806628e+08</td>\n",
       "      <td>2408000000</td>\n",
       "      <td>482322400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2194</th>\n",
       "      <td>2011/9/30</td>\n",
       "      <td>'002621</td>\n",
       "      <td>大连三垒</td>\n",
       "      <td>22.69</td>\n",
       "      <td>23.80</td>\n",
       "      <td>22.05</td>\n",
       "      <td>23.10</td>\n",
       "      <td>24.08</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-5.7724</td>\n",
       "      <td>26.7312</td>\n",
       "      <td>5354251</td>\n",
       "      <td>1.215095e+08</td>\n",
       "      <td>2269000000</td>\n",
       "      <td>454480700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2193</th>\n",
       "      <td>2011/10/10</td>\n",
       "      <td>'002621</td>\n",
       "      <td>大连三垒</td>\n",
       "      <td>23.60</td>\n",
       "      <td>24.18</td>\n",
       "      <td>22.36</td>\n",
       "      <td>22.66</td>\n",
       "      <td>22.69</td>\n",
       "      <td>0.91</td>\n",
       "      <td>4.0106</td>\n",
       "      <td>22.7764</td>\n",
       "      <td>4562109</td>\n",
       "      <td>1.065325e+08</td>\n",
       "      <td>2360000000</td>\n",
       "      <td>472708000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2192</th>\n",
       "      <td>2011/10/11</td>\n",
       "      <td>'002621</td>\n",
       "      <td>大连三垒</td>\n",
       "      <td>24.13</td>\n",
       "      <td>25.85</td>\n",
       "      <td>23.70</td>\n",
       "      <td>24.25</td>\n",
       "      <td>23.60</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2.2458</td>\n",
       "      <td>34.9752</td>\n",
       "      <td>7005537</td>\n",
       "      <td>1.723708e+08</td>\n",
       "      <td>2413000000</td>\n",
       "      <td>483323900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>2011/10/12</td>\n",
       "      <td>'002621</td>\n",
       "      <td>大连三垒</td>\n",
       "      <td>24.80</td>\n",
       "      <td>25.15</td>\n",
       "      <td>22.55</td>\n",
       "      <td>23.60</td>\n",
       "      <td>24.13</td>\n",
       "      <td>0.67</td>\n",
       "      <td>2.7766</td>\n",
       "      <td>30.1920</td>\n",
       "      <td>6047448</td>\n",
       "      <td>1.458584e+08</td>\n",
       "      <td>2480000000</td>\n",
       "      <td>496744000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020/10/12</td>\n",
       "      <td>'002621</td>\n",
       "      <td>美吉姆</td>\n",
       "      <td>6.96</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.72</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.86</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.4577</td>\n",
       "      <td>0.5032</td>\n",
       "      <td>4071930</td>\n",
       "      <td>2.812961e+07</td>\n",
       "      <td>5753573554</td>\n",
       "      <td>5632501229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020/10/13</td>\n",
       "      <td>'002621</td>\n",
       "      <td>美吉姆</td>\n",
       "      <td>6.95</td>\n",
       "      <td>6.96</td>\n",
       "      <td>6.84</td>\n",
       "      <td>6.95</td>\n",
       "      <td>6.96</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.1437</td>\n",
       "      <td>0.2279</td>\n",
       "      <td>1844460</td>\n",
       "      <td>1.275408e+07</td>\n",
       "      <td>5745306926</td>\n",
       "      <td>5624408555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020/10/14</td>\n",
       "      <td>'002621</td>\n",
       "      <td>美吉姆</td>\n",
       "      <td>6.85</td>\n",
       "      <td>7.16</td>\n",
       "      <td>6.84</td>\n",
       "      <td>6.93</td>\n",
       "      <td>6.95</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-1.4388</td>\n",
       "      <td>0.4038</td>\n",
       "      <td>3267859</td>\n",
       "      <td>2.279647e+07</td>\n",
       "      <td>5662640639</td>\n",
       "      <td>5543481813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020/10/15</td>\n",
       "      <td>'002621</td>\n",
       "      <td>美吉姆</td>\n",
       "      <td>7.04</td>\n",
       "      <td>7.23</td>\n",
       "      <td>6.90</td>\n",
       "      <td>7.02</td>\n",
       "      <td>6.85</td>\n",
       "      <td>0.19</td>\n",
       "      <td>2.7737</td>\n",
       "      <td>0.7743</td>\n",
       "      <td>6265798</td>\n",
       "      <td>4.435304e+07</td>\n",
       "      <td>5819706584</td>\n",
       "      <td>5697242623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020/10/16</td>\n",
       "      <td>'002621</td>\n",
       "      <td>美吉姆</td>\n",
       "      <td>7.07</td>\n",
       "      <td>7.19</td>\n",
       "      <td>6.97</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.4261</td>\n",
       "      <td>0.5747</td>\n",
       "      <td>4651108</td>\n",
       "      <td>3.302721e+07</td>\n",
       "      <td>5844506470</td>\n",
       "      <td>5721520645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2196 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              日期     股票代码    名称    收盘价    最高价    最低价    开盘价    前收盘    涨跌额  \\\n",
       "2195   2011/9/29  '002621   N三垒  24.08  25.15  22.88  23.98  24.00   0.08   \n",
       "2194   2011/9/30  '002621  大连三垒  22.69  23.80  22.05  23.10  24.08  -1.39   \n",
       "2193  2011/10/10  '002621  大连三垒  23.60  24.18  22.36  22.66  22.69   0.91   \n",
       "2192  2011/10/11  '002621  大连三垒  24.13  25.85  23.70  24.25  23.60   0.53   \n",
       "2191  2011/10/12  '002621  大连三垒  24.80  25.15  22.55  23.60  24.13   0.67   \n",
       "...          ...      ...   ...    ...    ...    ...    ...    ...    ...   \n",
       "4     2020/10/12  '002621   美吉姆   6.96   6.99   6.72   6.99   6.86    0.1   \n",
       "3     2020/10/13  '002621   美吉姆   6.95   6.96   6.84   6.95   6.96  -0.01   \n",
       "2     2020/10/14  '002621   美吉姆   6.85   7.16   6.84   6.93   6.95   -0.1   \n",
       "1     2020/10/15  '002621   美吉姆   7.04   7.23   6.90   7.02   6.85   0.19   \n",
       "0     2020/10/16  '002621   美吉姆   7.07   7.19   6.97   7.00   7.04   0.03   \n",
       "\n",
       "          涨跌幅      换手率       成交量          成交金额         总市值        流通市值  \n",
       "2195   0.3333  57.5282  11522900  2.806628e+08  2408000000   482322400  \n",
       "2194  -5.7724  26.7312   5354251  1.215095e+08  2269000000   454480700  \n",
       "2193   4.0106  22.7764   4562109  1.065325e+08  2360000000   472708000  \n",
       "2192   2.2458  34.9752   7005537  1.723708e+08  2413000000   483323900  \n",
       "2191   2.7766  30.1920   6047448  1.458584e+08  2480000000   496744000  \n",
       "...       ...      ...       ...           ...         ...         ...  \n",
       "4      1.4577   0.5032   4071930  2.812961e+07  5753573554  5632501229  \n",
       "3     -0.1437   0.2279   1844460  1.275408e+07  5745306926  5624408555  \n",
       "2     -1.4388   0.4038   3267859  2.279647e+07  5662640639  5543481813  \n",
       "1      2.7737   0.7743   6265798  4.435304e+07  5819706584  5697242623  \n",
       "0      0.4261   0.5747   4651108  3.302721e+07  5844506470  5721520645  \n",
       "\n",
       "[2196 rows x 15 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1963, 1)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set = data[::-1][['开盘价']].replace(0,np.nan)\n",
    "data_set.dropna(inplace=True)\n",
    "data_set = data_set.values.astype('float64')\n",
    "data_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1963, 1)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[23.98 23.1  22.66 ... 21.33 21.   19.67]\n",
      " [23.1  22.66 24.25 ... 21.   19.67 19.7 ]\n",
      " [22.66 24.25 23.6  ... 19.67 19.7  19.68]\n",
      " ...\n",
      " [ 7.64  7.75  7.4  ...  6.99  6.95  6.93]\n",
      " [ 7.75  7.4   7.22 ...  6.95  6.93  7.02]\n",
      " [ 7.4   7.22  7.34 ...  6.93  7.02  7.  ]]\n",
      "(1904, 60)\n"
     ]
    }
   ],
   "source": [
    "# 转化为可以用于监督学习的数据\n",
    "train_x, label_y = get_train_set(data_set, timesteps_in=timesteps_in, timesteps_out=timesteps_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1904, 30, 1), (1904, 30))"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape,label_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.98, 23.1 , 22.66, ...,  7.64,  7.75,  7.4 ])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg.iloc[:,0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "20/20 - 3s - loss: 242.6095\n",
      "Epoch 2/200\n",
      "20/20 - 3s - loss: 159.6451\n",
      "Epoch 3/200\n",
      "20/20 - 3s - loss: 105.8613\n",
      "Epoch 4/200\n",
      "20/20 - 3s - loss: 73.7354\n",
      "Epoch 5/200\n",
      "20/20 - 3s - loss: 54.9583\n",
      "Epoch 6/200\n",
      "20/20 - 3s - loss: 44.2658\n",
      "Epoch 7/200\n",
      "20/20 - 3s - loss: 38.7165\n",
      "Epoch 8/200\n",
      "20/20 - 3s - loss: 35.7378\n",
      "Epoch 9/200\n",
      "20/20 - 3s - loss: 33.6893\n",
      "Epoch 10/200\n",
      "20/20 - 3s - loss: 32.8759\n",
      "Epoch 11/200\n",
      "20/20 - 3s - loss: 31.8194\n",
      "Epoch 12/200\n",
      "20/20 - 4s - loss: 31.9232\n",
      "Epoch 13/200\n",
      "20/20 - 3s - loss: 31.6072\n",
      "Epoch 14/200\n",
      "20/20 - 3s - loss: 31.9212\n",
      "Epoch 15/200\n",
      "20/20 - 3s - loss: 31.6710\n",
      "Epoch 16/200\n",
      "20/20 - 3s - loss: 31.5106\n",
      "Epoch 17/200\n",
      "20/20 - 4s - loss: 31.2176\n",
      "Epoch 18/200\n",
      "20/20 - 4s - loss: 31.1673\n",
      "Epoch 19/200\n",
      "20/20 - 4s - loss: 31.6369\n",
      "Epoch 20/200\n",
      "20/20 - 4s - loss: 31.5939\n",
      "Epoch 21/200\n",
      "20/20 - 4s - loss: 31.7360\n",
      "Epoch 22/200\n",
      "20/20 - 4s - loss: 31.5841\n",
      "Epoch 23/200\n",
      "20/20 - 4s - loss: 31.7041\n",
      "Epoch 24/200\n",
      "20/20 - 3s - loss: 31.5349\n",
      "Epoch 25/200\n",
      "20/20 - 3s - loss: 31.3065\n",
      "Epoch 26/200\n",
      "20/20 - 3s - loss: 31.3953\n",
      "Epoch 27/200\n",
      "20/20 - 3s - loss: 31.6091\n",
      "Epoch 28/200\n",
      "20/20 - 3s - loss: 31.4533\n",
      "Epoch 29/200\n",
      "20/20 - 3s - loss: 32.0240\n",
      "Epoch 30/200\n",
      "20/20 - 3s - loss: 31.4946\n",
      "Epoch 31/200\n",
      "20/20 - 3s - loss: 31.6487\n",
      "Epoch 32/200\n",
      "20/20 - 3s - loss: 31.4354\n",
      "Epoch 33/200\n",
      "20/20 - 3s - loss: 31.6355\n",
      "Epoch 34/200\n",
      "20/20 - 3s - loss: 31.5715\n",
      "Epoch 35/200\n",
      "20/20 - 3s - loss: 31.8751\n",
      "Epoch 36/200\n",
      "20/20 - 3s - loss: 32.1258\n",
      "Epoch 37/200\n",
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     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_21\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_42 (LSTM)               (None, 30, 128)           66560     \n",
      "_________________________________________________________________\n",
      "lstm_43 (LSTM)               (None, 128)               131584    \n",
      "_________________________________________________________________\n",
      "dropout_21 (Dropout)         (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_21 (Dense)             (None, 30)                3870      \n",
      "=================================================================\n",
      "Total params: 202,014\n",
      "Trainable params: 202,014\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用LSTM进行训练、预测\n",
    "lstm_model(data_set, train_x, label_y, epochs, batch_size, timesteps_out=timesteps_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.0 64-bit ('Bi_env': venv)",
   "language": "python",
   "name": "python38064bitbienvvenvba07af95a1bb4b078aa8134bba84dff2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
